Winding tension control method

Abstract

The invention relates to a winding tension control method and system. The method is applied to a winding tension control device, the device comprises a tension control part and a linear speed control part, and the method comprises the following steps: acquiring a real-time tension detection value and a tension set value of the tension control part, calculating a tension error and a tension error change rate, and performing fuzzy reasoning according to a fuzzy control algorithm to obtain a linear speed control value; pID output parameters are obtained through integral separation PID and used for indicating and controlling a first servo motor of a tension control part; and acquiring a real-time linear speed value and a linear speed set value of the linear speed control part, calculating a linear speed error and a linear speed error change rate, and inputting the linear speed error and the linear speed error change rate into the PID compensation model based on a fuzzy control algorithm to obtain a PID compensation parameter for indicating and controlling a second servo motor of the linear speed control part. According to the method, the tension and the linear speed are subjected to fuzzy control and PID control respectively, the control precision can be improved, and the stability, the response speed and the production efficiency of the winding process are effectively improved.

Description

Technical Field

[0001] The invention belongs to the field of computer and communication technology, and in particular relates to a winding tension control method and system. Background Art

[0002] The tension system is a key equipment in many production lines and is widely used in printing, paper products, textiles and other industries. Its core function is to maintain the stability of the tension during the winding process. The quality of winding directly affects the quality of the product and production efficiency. Traditional tension control methods mainly rely on a single PID control method, which can ensure the basic stability of the tension control system by feedback adjustment of real-time tension and line speed errors. However, the PID control method mainly relies on fixed proportional, integral, and differential parameters. When faced with environmental changes, load fluctuations, or the dynamic characteristics of the tension control system, it is difficult to adapt quickly, and it is easy to cause over-adjustment or under-adjustment problems. In addition, it lacks the ability to handle system fuzziness and uncertainty, and it is difficult to handle complex control requirements. Summary of the invention

[0003] Based on this, it is necessary to address the above-mentioned technical problems. The purpose of the present invention is to provide a winding tension control method and system to improve the tension control accuracy and optimize the response speed of the tension system, thereby achieving more accurate and stable tension and line speed control.

[0004] In a first aspect, the present application provides a winding tension control method, which is applied to a winding tension control device, the winding tension control device comprising a tension control part and a line speed control part, and the method comprises the following steps:

[0005] Acquire the real-time tension detection value and tension setting value of the tension control part, and calculate the tension error and tension error change rate based on the real-time tension detection value and tension setting value;

[0006] Acquire the real-time linear speed value and the linear speed setting value of the linear speed control part, and calculate the linear speed error and the linear speed error change rate based on the real-time linear speed value and the linear speed setting value;

[0007] Perform fuzzy reasoning on the tension error and the tension error change rate according to the fuzzy control algorithm, and use integral separation PID to obtain PID output parameters, and the PID output parameters are used to indicate the first servo motor that controls the tension control part;

[0008] Based on the fuzzy control algorithm, the linear speed error and the linear speed error change rate are input into the PID compensation model to obtain the PID compensation parameters, and the PID compensation parameters are used to indicate the second servo motor that controls the linear speed control part.

[0009] In one embodiment, a fuzzy control algorithm is used to input the linear velocity error and the linear velocity error change rate into a PID compensation model, and the PID compensation parameters obtained include:

[0010] The linear velocity error and the linear velocity error change rate are converted into linguistic variables using a Gaussian membership function;

[0011] Based on the preset fuzzy rule base, the language variables are processed in a hierarchical manner to obtain the fuzzy control output;

[0012] According to the Sugeno type fuzzy reasoning mechanism, the fuzzy control output is inferred through the preset rule base to obtain the output fuzzy set;

[0013] The output fuzzy set is converted into numerical value through defuzzification method to obtain PID compensation parameters.

[0014] In one embodiment, fuzzy reasoning is performed on the tension error and the tension error change rate according to the fuzzy control algorithm, and the integral separation PID is used to obtain the PID output parameters, including:

[0015] Based on the fuzzy control algorithm, fuzzy reasoning is performed on the tension error and the tension error change rate to obtain the PID parameter adjustment amount;

[0016] Adjust the current PID parameters according to the PID parameter adjustment amount to obtain updated PID parameters;

[0017] The integral separation threshold is calculated based on the tension error using the following preset function:

[0018] ∈(k)=∈ 0 ·(1+α·tanh(β|e(k)|))

[0019] Among them, ∈(k) is the result of the preset function, that is, the integral separation threshold at time k, ∈ 0 is the preset initial integral separation threshold, α and β are proportional coefficients, and e(k) is the tension error at time k;

[0020] According to the integral separation threshold and the preset judgment formula, the integral action of the integral separation PID is dynamically adjusted, and the PID output parameters are calculated based on the updated PID parameters.

[0021] In one embodiment, the preset determination formula is:

[0022]

[0023] Among them, u I (k) is the result of the preset judgment formula, that is, the integral action output at time k, K i (k) is the integration coefficient at time k, is the accumulated value of tension error at time 0-k; otherwise is other.

[0024] In one embodiment, the method further comprises:

[0025] According to the PID compensation parameters, the integral separation conditions are dynamically adjusted to obtain the optimized PID control parameters;

[0026] The optimized PID control parameters are used as the input layer nodes of the RBF neural network, and the hidden layer nodes of the BF neural network are calculated by Gaussian radial basis function to obtain the output PID compensation parameters.

[0027] The output PID compensation parameters are used as the final PID compensation parameters.

[0028] In one embodiment, the method further comprises:

[0029] Control the servo motor of the line speed control part according to the PID compensation parameters to adjust the line speed during winding;

[0030] The adjusted line speed value is obtained, and the adjusted line speed value and the type of winding craftwork are used as training sets. The connection weights of the RBF neural network are optimized according to the recursive least squares method to obtain the optimized RBF neural network, and the optimized RBF neural network is used to update the RBF neural network.

[0031] In one of the embodiments, the method further includes optimizing a preset fuzzy rule base and a Gaussian membership function by a genetic algorithm if the linear velocity error in three consecutive sampling periods exceeds a preset safety threshold;

[0032] Among them, the optimization of the preset fuzzy rule base and Gaussian membership function by genetic algorithm includes:

[0033] Encode the preset fuzzy rule base and Gaussian membership function to obtain the initialized population;

[0034] The inverse of the sum of squares of linear velocity errors in multiple consecutive sampling periods is used as the fitness function;

[0035] Based on the preset number of iterations, the fitness function and the initialized population are genetically operated to obtain a new population;

[0036] The fitness value of the new population is calculated according to the fitness function to obtain the optimal individual, and the preset fuzzy rule base and Gaussian membership function corresponding to the optimal individual are used to update the PID compensation model to obtain an updated PID compensation model.

[0037] In a second aspect, the present application further provides 8. A winding tension control system, applied to a tension control device, the winding tension control device comprising a tension control part and a line speed control part, the system comprising:

[0038] A tension data analysis module is used to obtain the real-time tension detection value and tension setting value of the tension control part, and calculate the tension error and tension error change rate based on the real-time tension detection value and tension setting value;

[0039] A line speed data analysis module, used to obtain the real-time line speed value and the line speed setting value of the line speed control part, and calculate the line speed error and the line speed error change rate based on the real-time line speed value and the line speed setting value;

[0040] A tension control module, used for performing fuzzy reasoning on the tension error and the tension error change rate according to a fuzzy control algorithm, and obtaining a PID output parameter by using integral separation PID, wherein the PID output parameter is used for indicating a first servo motor for controlling a tension control part;

[0041] The linear speed control module is used to input the linear speed error and the linear speed error change rate into the PID compensation model based on the fuzzy control algorithm to obtain the PID compensation parameters, and the PID compensation parameters are used to indicate the second servo motor that controls the linear speed control part.

[0042] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the first aspect when executing the computer program.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in the first aspect when executed by a processor.

[0044] The above-mentioned winding tension control method and system, computer equipment and storage medium are applied to a winding tension control device, which includes a tension control part and a line speed control part. Specifically, the method can monitor the tension change in real time by obtaining the real-time tension detection value and tension setting value of the tension control part, and can combine the fuzzy control algorithm to infer the tension error and error change rate, so that the tension control process can more flexibly adapt to the fluctuations and uncertainties in the production process, thereby improving the tension control accuracy. And by processing the fuzzy reasoning output result through integral separation PID, the PID output parameter is obtained, and then the first servo motor of the tension control part can be adjusted to further accurately control the winding tension. In addition, the line speed control part can monitor the line speed change in real time by obtaining the real-time line speed value and line speed setting value of the line speed control part, and combine the fuzzy control algorithm and the PID compensation model to input the line speed error and change rate into the PID compensation model, so as to dynamically adjust the PID compensation parameter. The PID compensation parameter is used to adjust the second servo motor of the line speed control part, so that the adjustment of the line speed is more accurate and efficient, thereby avoiding the tension control instability caused by line speed fluctuations.

[0045] Compared with the traditional tension control method, this method further optimizes the control process of tension and line speed by combining fuzzy control and PID algorithm for real-time feedback adjustment of tension and line speed in the tension control part and line speed control part of the winding tension control device, and improves the response speed and robustness of tension control, thereby achieving stable operation and higher production efficiency in a dynamically changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A flow chart of a winding tension control method provided for an exemplary embodiment of the present invention;

[0048] Figure 2 A schematic structural diagram of a winding tension control system provided as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] In one embodiment, Figure 1 As shown, a winding tension control method is provided, which is applied to a winding tension control device, and the winding tension control device includes a tension control part and a line speed control part. This embodiment uses the method applied to a terminal as an example for illustration, and it can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0051] S101: Acquire a real-time tension detection value and a tension setting value of a tension control part, and calculate a tension error and a tension error change rate based on the real-time tension detection value and the tension setting value.

[0052] Specifically, the tension control part may include a first winding roller for retracting / releasing, a first servo motor, a tension sensor and a floating roller. The tension sensor is followed by a floating roller, the first winding roller is driven by a first servo motor, the floating roller is used to buffer the tension, and the actual tension borne by the material during the winding process, that is, the real-time tension detection value, can be measured in real time through the tension sensor. Optionally, the tension sensor can be a piezoelectric sensor. And the tension error can be obtained by comparing the real-time tension detection value with the tension setting value. The tension error quantifies the gap between the actual tension and the expected tension in the current winding process, and the tension error change rate can be obtained by differentiating the tension error. The tension error change rate reflects the trend of the tension error over time, which helps to further predict the change trend of the tension, so that the control strategy can be adjusted in time and accurately to maintain a stable winding tension.

[0053] S102: Acquire a real-time linear speed value and a linear speed setting value of a linear speed control part, and calculate a linear speed error and a linear speed error change rate based on the real-time linear speed value and the linear speed setting value.

[0054] Schematically, the linear speed control part may include a second roller for retracting / releasing, a second servo motor, an encoder and a process area. Among them, the second roller is driven by a second servo motor, and the encoder is followed by a process area. The process area can add corresponding production processes according to the production process requirements. And it can be an incremental encoder, and its angular velocity feedback value can be read, and combined with the encoder radius for calculation to obtain the linear speed size, that is, the real-time linear speed value. The real-time linear speed value reflects the actual speed of the material passing through the process area at the current moment. In addition, the linear speed error can be calculated in combination with the linear speed setting value, which helps to understand the degree of deviation of the current linear speed control and provide an accurate basis for subsequent speed adjustment. And the linear speed error can be differentiated to obtain the linear speed error change rate, so as to further understand the trend of the linear speed error over time.

[0055] S103: Perform fuzzy reasoning on the tension error and the tension error change rate according to the fuzzy control algorithm, and use integral separation PID to obtain PID output parameters, and the PID output parameters are used to indicate the first servo motor that controls the tension control part.

[0056] Fuzzy control algorithm is a control method based on fuzzy logic. In winding tension control, the complex nonlinear relationship between tension error and tension error change rate and the control quantity that needs to be adjusted can be effectively handled by fuzzy control algorithm. For example, fuzzy rules can be designed based on process experience and knowledge, and fuzzy reasoning can be performed on tension error and tension error change rate through the fuzzy rules to obtain fuzzy reasoning output results. In addition, integral separation PID (Proportional-Integral-Derivative, PID) is a feedback control algorithm composed of proportional terms, integral terms and differential terms. By combining the fuzzy reasoning output results obtained by fuzzy reasoning and the tension error and tension error change rate, the proportional, integral and differential coefficients can be dynamically adjusted according to the size of the error to adapt to different working conditions, and the PID output parameters can be obtained. The first servo motor adjusts its output torque or speed by receiving the PID output parameters, thereby changing the tension of the tension control part.

[0057] S104: Based on the fuzzy control algorithm, the linear speed error and the linear speed error change rate are input into a PID compensation model to obtain a PID compensation parameter, and the PID compensation parameter is used to indicate the second servo motor that controls the linear speed control part.

[0058] By inputting the line speed error and the rate of change of the line speed error into the PID compensation model through the fuzzy control algorithm, the fuzzy logic can be used to process the uncertainty and nonlinear relationship in the process of line speed adjustment, and the line speed control quantity obtained by fuzzy control, as well as the line speed error and the rate of change of the line speed error, the parameters of the PID controller are dynamically adjusted and compensated to obtain the PID compensation parameters. After receiving the PID compensation parameters, the second servo motor can adjust its output speed accordingly. For example, if the PID compensation parameters require an increase in the motor speed, the second servo motor can increase the output power to speed up the line speed; if the speed is required to be reduced, the second servo motor can reduce the output power and reduce the line speed. Through this process, the line speed in the winding process can be accurately controlled to make it as close to the line speed setting value as possible, ensuring the stability of the winding process and the product quality.

[0059] In the above-mentioned winding tension control method and system, the tension control part and the line speed control part of the winding tension control device are controlled respectively. In the tension control part, by obtaining the real-time tension detection value and tension setting value of the tension control part, the tension error and its rate of change can be calculated in real time, and then the fuzzy control algorithm can be combined for reasoning to achieve precise adjustment of the tension control part. In addition, the tension control part can also be combined with the integral separation PID control algorithm to process the fuzzy reasoning output result, obtain the PID output parameter, and then control the first servo motor of the tension control part, so as to improve the control accuracy of the winding tension, avoid the over-adjustment or under-adjustment problem in the traditional PID control method, and ensure the stability and accuracy of the tension.

[0060] In the line speed control part, the line speed error and error change rate are calculated by obtaining the real-time line speed value and line speed setting value of the line speed control part in real time, and the line speed error and line speed error change rate are input into the PID compensation model through the fuzzy control algorithm to obtain the PID compensation parameters of the second servo motor of the line speed control part, so as to make the adjustment of the line speed more accurate and avoid the situation of unstable tension caused by line speed fluctuation. This method further improves the control accuracy of tension and line speed by combining fuzzy control and PID control algorithms to control the real-time tension and line speed, effectively solves the problems of tension fluctuation and unstable line speed control, improves the stability and response speed of the system, and can optimize the winding process and improve production efficiency.

[0061] In one exemplary embodiment, a fuzzy control algorithm is used to input the linear velocity error and the linear velocity error change rate into a PID compensation model, and the PID compensation parameters obtained include:

[0062] The linear velocity error and the linear velocity error change rate are converted into linguistic variables using a Gaussian membership function;

[0063] Based on the preset fuzzy rule base, the language variables are processed in a hierarchical manner to obtain the fuzzy control output;

[0064] According to the Sugeno type fuzzy reasoning mechanism, the fuzzy control output is inferred through the preset rule base to obtain the output fuzzy set;

[0065] The output fuzzy set is converted into numerical value through defuzzification method to obtain PID compensation parameters.

[0066] Specifically, in fuzzy control, the membership function is used to describe the degree to which an exact value belongs to a fuzzy set. The Gaussian membership function has the characteristics of smoothness and unimodality, which can better reflect the fuzziness in actual situations. The membership of the linear speed error and the linear speed error change rate to each fuzzy set is calculated according to the preset Gaussian membership function, that is, converted into the corresponding language variable. According to the converted language variable, the matching rules are searched in the preset fuzzy rule library, and for each matching rule, the activation degree of the rule can be determined by logical operation according to the membership of the language variable in its premise, and the membership of the corresponding fuzzy control output can be obtained according to the conclusion. The membership of the fuzzy control output of all activated rules is combined to obtain the fuzzy control output. Schematically, compared with other fuzzy reasoning methods, the rule conclusion part of Sugeno type fuzzy reasoning is a linear combination or constant of the input variables, which is suitable for the generation of PID compensation parameters and has a small amount of calculation. In addition, the output fuzzy set obtained by reasoning can be converted into precise numerical values ​​through defuzzification methods such as the center of gravity method and the maximum membership method to obtain PID compensation parameters, which can then control the second servo motor of the line speed control part to achieve precise control of the winding line speed.

[0067] In one exemplary embodiment, fuzzy reasoning is performed on the tension error and the tension error change rate according to the fuzzy control algorithm, and the integral separation PID is used to obtain the PID output parameters, including:

[0068] Based on the fuzzy control algorithm, fuzzy reasoning is performed on the tension error and the tension error change rate to obtain the PID parameter adjustment amount;

[0069] Adjust the current PID parameters according to the PID parameter adjustment amount to obtain updated PID parameters;

[0070] The integral separation threshold is calculated based on the tension error using the following preset function:

[0071] ∈(k)=∈ 0 ·(1+α·tanh(β|e(k)|))

[0072] Among them, ∈(k) is the result of the preset function, that is, the integral separation threshold at time k, ∈ 0 is the preset initial integral separation threshold, α and β are proportional coefficients, and e(k) is the tension error at time k;

[0073] According to the integral separation threshold and the preset judgment formula, the integral action of the integral separation PID is dynamically adjusted, and the PID output parameters are calculated based on the updated PID parameters.

[0074] Specifically, after the tension error and the rate of change of the tension error are subjected to fuzzification, fuzzy reasoning and defuzzification, the PID parameter adjustment amount can be obtained. The PID parameter adjustment amount can reflect the degree and direction to which the current PID parameters need to be adjusted according to the current tension error and rate of change. After obtaining the PID parameter adjustment amount, the current PID parameters can be adjusted by operations such as addition or multiplication, so as to obtain updated PID parameters to make them more suitable for the current tension control working conditions. Subsequently, the integral separation threshold corresponding to the tension error at the current moment can be calculated by a preset function. The threshold will change dynamically with the change of the tension error, making the change of the integral separation threshold more reasonable. Based on the threshold, a preset judgment formula can be used to determine whether to enable or disable the integral action of the integral separation PID, thereby obtaining the corresponding PID output parameters.

[0075] In one exemplary embodiment, the preset determination formula is:

[0076]

[0077] Among them, u I (k) is the result of the preset judgment formula, that is, the integral action output at time k, K i (k) is the integration coefficient at time k, is the accumulated value of tension error at time 0-k; otherwise is other.

[0078] Specifically, the above formula determines whether to enable the integral action and calculates the integral action output u according to the relationship between the tension error |e(k)| at time k and the integral separation threshold ∈(k). I (k). When the tension error |e(k)| is within a certain range, that is, |e(k)|≤∈(k), the integral action is enabled to calculate the output; when the tension error |e(k)| exceeds this range, the integral action is turned off and the output is 0.

[0079] In one of the exemplary embodiments, the method further comprises:

[0080] According to the PID compensation parameters, the integral separation conditions are dynamically adjusted to obtain the optimized PID control parameters;

[0081] The optimized PID control parameters are used as the input layer nodes of the RBF neural network, and the hidden layer nodes of the BF neural network are calculated by Gaussian radial basis function to obtain the output PID compensation parameters.

[0082] The output PID compensation parameters are used as the final PID compensation parameters.

[0083] Indicatively, the traditional integral separation PID control can improve the control performance to a certain extent, but in the face of complex and changeable working conditions such as changes in material properties, fixed integral separation conditions may not guarantee the best control effect of the entire winding process. Then this embodiment can dynamically adjust the integral separation conditions according to the PID compensation parameters to obtain optimized PID control parameters. In addition, the RBF (Radial Basis Function, RBF) neural network is a feedforward neural network, which can process and optimize the nonlinear mapping ability by inputting the optimized PID control parameters into the RBF neural network, and thus can more accurately reflect the current required control compensation amount. The output PID compensation parameters obtained comprehensively consider the real-time state of the system, the optimization of integral separation, and the nonlinear mapping ability of the neural network, and use them as the final PID compensation parameters to control the second servo motor of the linear speed control part, which can make the control of the motor more accurate and stable, thereby improving the winding quality and production efficiency.

[0084] Furthermore, the method further comprises:

[0085] Control the servo motor of the line speed control part according to the PID compensation parameters to adjust the line speed during winding;

[0086] The adjusted line speed value is obtained, and the adjusted line speed value and the type of winding craftwork are used as training sets. The connection weights of the RBF neural network are optimized according to the recursive least squares method to obtain the optimized RBF neural network, and the optimized RBF neural network is used to update the RBF neural network.

[0087] Specifically, the adjusted linear speed value reflects the actual linear speed achieved by the winding process after adjustment by the second servo motor, and includes the operating effect information under the current control strategy. The type of winding craftwork can reflect different process requirements and winding characteristics. Combining the two into a training set can provide rich and targeted data for the training of the RBF neural network. The recursive least squares method is an online adaptive filtering algorithm that can quickly update the parameters of the model every time new training data is obtained to minimize the prediction error. In the RBF neural network, the connection weight determines the information transmission and mapping relationship between the input layer, hidden layer and output layer. The recursive least squares method can be used to continuously adjust the connection weights of the RBF neural network according to the data in the training set, which can better adapt to the actual operation of the current winding process and the winding requirements of different craftworks. The optimized RBF network replaces the original RBF neural network, and in the subsequent winding process, the control accuracy and stability of the winding system are further improved, and the adaptive optimization control of the winding process is realized.

[0088] In one of the exemplary embodiments, the method further includes optimizing a preset fuzzy rule base and a Gaussian membership function by a genetic algorithm if the linear velocity error in three consecutive sampling periods exceeds a preset safety threshold;

[0089] Among them, the optimization of the preset fuzzy rule base and Gaussian membership function by genetic algorithm includes:

[0090] Encode the preset fuzzy rule base and Gaussian membership function to obtain the initialized population;

[0091] The inverse of the sum of squares of linear velocity errors in multiple consecutive sampling periods is used as the fitness function;

[0092] Based on the preset number of iterations, the fitness function and the initialized population are genetically operated to obtain a new population;

[0093] The fitness value of the new population is calculated according to the fitness function to obtain the optimal individual, and the preset fuzzy rule base and Gaussian membership function corresponding to the optimal individual are used to update the PID compensation model to obtain an updated PID compensation model.

[0094] Specifically, when the linear speed error in three consecutive sampling periods exceeds the preset safety threshold, it means that the linear speed control effect in the current winding process is not good, there may be a large deviation, and the control strategy needs to be adjusted. During the adjustment process, the preset fuzzy rule base and Gaussian membership function can be optimized by genetic algorithm. The rules and Gaussian membership function in the preset fuzzy rule base are digitally encoded and converted into individuals to construct an initial population. During the optimization process, the fitness function is used to evaluate the quality of each individual in the population and guide the genetic algorithm to search in the direction of optimization. The sum of squares of linear speed errors reflects the cumulative degree of deviation of the system linear speed from the set value over a period of time. The smaller the value, the better the control effect. That is, the inverse of the sum of squares of linear speed errors in multiple consecutive sampling periods can be used as the fitness function. The larger the fitness value, the better the control strategy corresponding to the individual. Through the preset number of iterations, the fitness function and the initial population are subjected to genetic operations such as selection, crossover and mutation to obtain a new population. The new population contains more individuals with excellent characteristics. The fitness value of each individual in the new population can be calculated to screen out the individual with the largest fitness value, that is, the optimal individual. The preset fuzzy rule base and Gaussian membership function corresponding to the optimal individual are the optimal control strategy after genetic algorithm optimization. The PID compensation model is updated through the optimal rule base and membership function to obtain an updated PID compensation model, which can more accurately calculate the PID compensation parameters according to the line speed error and error change rate, thereby better controlling the servo motor of the line speed control part and improving the stability and control accuracy of the line speed control in the winding process.

[0095] Exemplarily, the tension control part and the line speed control part can implement fuzzy control through a PLC (Programmable Logic Controller, PLC) programmable logic controller to further improve the real-time performance of the tension control and the line speed.

[0096] Based on the same inventive concept, Figure 2 As shown, the embodiment of the present application further provides a winding tension control system 200, which is applied to a tension control device. The winding tension control device includes a tension control part and a line speed control part. The system includes:

[0097] The tension data analysis module 201 is used to obtain the real-time tension detection value and tension setting value of the tension control part, and calculate the tension error and tension error change rate based on the real-time tension detection value and tension setting value;

[0098] The linear speed data analysis module 202 is used to obtain the real-time linear speed value and the linear speed setting value of the linear speed control part, and calculate the linear speed error and the linear speed error change rate based on the real-time linear speed value and the linear speed setting value;

[0099] The tension control module 203 is used to perform fuzzy reasoning on the tension error and the tension error change rate according to the fuzzy control algorithm, and use integral separation PID to obtain PID output parameters, and the PID output parameters are used to indicate the first servo motor that controls the tension control part;

[0100] The linear speed control module 204 is used to input the linear speed error and the linear speed error change rate into the PID compensation model based on the fuzzy control algorithm to obtain the PID compensation parameters, and the PID compensation parameters are used to instruct the second servo motor to control the linear speed control part.

[0101] Specifically, the tension data analysis module 201 obtains the real-time tension detection value and tension setting value of the tension control part, calculates the tension error and the rate of change of the tension error, and provides accurate input data for subsequent tension control. And the tension control module 203 can perform fuzzy reasoning on the tension error and its rate of change through the fuzzy control algorithm, and can obtain the PID output parameters, and can adjust the first servo motor of the tension control part according to the PID output parameters, so as to accurately control the winding tension. This process effectively avoids the problem of tension fluctuation or instability, improves the response speed and stability of tension control, and can maintain a high accuracy, especially when facing load changes in the production process.

[0102] The line speed data analysis module 202 can obtain the real-time line speed value and line speed setting value of the line speed control part, and calculate the line speed error and its change rate. In the line speed control module 204, the line speed error and its change rate can be input into the PID compensation model through the fuzzy control algorithm to obtain the PID compensation parameters, and then the second servo motor of the line speed control part can be adjusted, and the line speed can be accurately adjusted under variable production conditions, avoiding the tension instability caused by line speed fluctuations, and further improving the line speed control accuracy. Through the collaboration of various modules, the system combines the fuzzy control algorithm and the PID control algorithm for the tension and line speed of the winding tension control device, thereby improving the tension and line speed control accuracy of the winding process and ensuring the stability and consistency of the system.

[0103] Furthermore, the line speed control module 204 includes:

[0104] A data conversion subunit, for converting the linear velocity error and the linear velocity error change rate into a linguistic variable using a Gaussian membership function;

[0105] A grading subunit is used to perform grading processing on the language variables based on a preset fuzzy rule base to obtain a fuzzy control output;

[0106] The reasoning subunit is used to reason the fuzzy control output quantity through a preset rule base according to the Sugeno type fuzzy reasoning mechanism to obtain an output fuzzy set;

[0107] The defuzzification subunit is used to convert the output fuzzy set into a numerical value through a defuzzification method to obtain the PID compensation parameters.

[0108] Furthermore, the tension control module 203 includes:

[0109] The tension fuzzy reasoning subunit is used to perform fuzzy reasoning on the tension error and the tension error change rate based on the fuzzy control algorithm to obtain the PID parameter adjustment amount;

[0110] Integral separation adjustment subunit, used for:

[0111] Adjust the current PID parameters according to the PID parameter adjustment amount to obtain updated PID parameters;

[0112] The integral separation threshold is calculated based on the tension error using the following preset function:

[0113] ∈(k)=∈ 0 ·(1+α·tanh(β|e(k)|))

[0114] Among them, ∈(k) is the result of the preset function, that is, the integral separation threshold at time k, ∈ 0 is the preset initial integral separation threshold, α and β are proportional coefficients, and e(k) is the tension error at time k;

[0115] According to the integral separation threshold and the preset judgment formula, the integral action of the integral separation PID is dynamically adjusted, and the PID output parameters are calculated based on the updated PID parameters.

[0116] Exemplarily, the preset determination formula is:

[0117]

[0118] Among them, u I (k) is the result of the preset judgment formula, that is, the integral action output at time k, K i (k) is the integration coefficient at time k, is the accumulated value of tension error at time 0-k; otherwise is other.

[0119] Furthermore, the system also includes a compensation optimization module, which is used to:

[0120] According to the PID compensation parameters, the integral separation conditions are dynamically adjusted to obtain the optimized PID control parameters;

[0121] The optimized PID control parameters are used as the input layer nodes of the RBF neural network, and the hidden layer nodes of the BF neural network are calculated by Gaussian radial basis function to obtain the output PID compensation parameters.

[0122] The output PID compensation parameters are used as the final PID compensation parameters.

[0123] Furthermore, the compensation optimization module also includes a feedback optimization subunit, which is used to:

[0124] Control the servo motor of the line speed control part according to the PID compensation parameters to adjust the line speed during winding;

[0125] The adjusted line speed value is obtained, and the adjusted line speed value and the type of winding craftwork are used as training sets. The connection weights of the RBF neural network are optimized according to the recursive least squares method to obtain the optimized RBF neural network, and the optimized RBF neural network is used to update the RBF neural network.

[0126] Furthermore, the system also includes a line speed optimization module for:

[0127] If the linear speed error exceeds the preset safety threshold within three consecutive sampling periods, the preset fuzzy rule base and Gaussian membership function are optimized by genetic algorithm;

[0128] Among them, the optimization of the preset fuzzy rule base and Gaussian membership function by genetic algorithm includes:

[0129] Encode the preset fuzzy rule base and Gaussian membership function to obtain the initialized population;

[0130] The inverse of the sum of squares of linear velocity errors in multiple consecutive sampling periods is used as the fitness function;

[0131] Based on the preset number of iterations, the fitness function and the initialized population are genetically operated to obtain a new population;

[0132] The fitness value of the new population is calculated according to the fitness function to obtain the optimal individual, and the preset fuzzy rule base and Gaussian membership function corresponding to the optimal individual are used to update the PID compensation model to obtain an updated PID compensation model.

[0133] In an exemplary embodiment, the present invention further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a winding tension control method and system of the present application are implemented. A multi-core processor is preferred to improve the parallel processing capability of the system. Memory: Provides sufficient temporary storage space to support the operation of the program and the processing of data. The memory capacity should be large enough to accommodate a large amount of supply information and computing tasks.

[0134] In an exemplary embodiment, the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of a winding tension control method and system of the present application are implemented. The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid state drive (SSD), or an optical disk, etc. Among them, the random access memory may include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0135] The above-mentioned embodiments only express several implementation methods of the embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the embodiments of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the embodiments of the present application, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A winding tension control method, characterized in that: Applied to a winding tension control device, the winding tension control device comprises a tension control part and a line speed control part, and the method comprises the following steps: Acquiring a real-time tension detection value and a tension setting value of the tension control part, and calculating a tension error and a tension error change rate based on the real-time tension detection value and the tension setting value; Acquire a real-time linear speed value and a linear speed setting value of the linear speed control part, and calculate a linear speed error and a linear speed error change rate based on the real-time linear speed value and the linear speed setting value; Performing fuzzy reasoning on the tension error and the tension error change rate according to a fuzzy control algorithm, and using integral separation PID to obtain a PID output parameter, wherein the PID output parameter is used to indicate a first servo motor that controls the tension control part; Based on the fuzzy control algorithm, the linear speed error and the linear speed error change rate are input into a PID compensation model to obtain a PID compensation parameter, and the PID compensation parameter is used to indicate the second servo motor that controls the linear speed control part.

2. The method according to claim 1, characterized in that The fuzzy control algorithm is used to input the linear speed error and the linear speed error change rate into the PID compensation model to obtain the PID compensation parameters including: The linear velocity error and the linear velocity error change rate are converted into linguistic variables using a Gaussian membership function; Based on a preset fuzzy rule base, the language variables are processed in a hierarchical manner to obtain a fuzzy control output; Reasoning the fuzzy control output quantity through the preset rule base according to the Sugeno type fuzzy reasoning mechanism to obtain an output fuzzy set; The output fuzzy set is converted into a numerical value by a defuzzification method to obtain the PID compensation parameter.

3. The method according to claim 1, characterized in that The method of performing fuzzy reasoning on the tension error and the tension error change rate according to the fuzzy control algorithm and obtaining PID output parameters by using integral separation PID includes: Perform fuzzy reasoning on the tension error and the tension error change rate based on a fuzzy control algorithm to obtain a PID parameter adjustment amount; Adjust the current PID parameters according to the PID parameter adjustment amount to obtain updated PID parameters; The integral separation threshold is calculated based on the tension error using the following preset function: ∈(k)=∈0·(1+α·tanh(β|e(k)|)) Wherein, ∈(k) is the result of the preset function, that is, it represents the integral separation threshold at time k, ∈0 is the preset initial integral separation threshold, α and β are proportional coefficients, and e(k) is the tension error at time k; According to the integral separation threshold and a preset determination formula, the integral action of the integral separation PID is dynamically adjusted, and based on the updated PID parameters, the PID output parameters are calculated.

4. The method according to claim 3, characterized in that The preset determination formula is: Among them, u I (k) is the result of the preset judgment formula, that is, the integral action output at time k, K i (k) is the integration coefficient at time k, is the accumulated value of tension error at time 0-k; otherwise is other.

5. The method according to claim 1, characterized in that The method further comprises: According to the PID compensation parameters, the integral separation condition is dynamically adjusted to obtain the optimized PID control parameters; The optimized PID control parameters are used as input layer nodes of the RBF neural network, and the hidden layer nodes of the RBF neural network are calculated by Gaussian radial basis function to obtain output PID compensation parameters; The output PID compensation parameter is used as the final PID compensation parameter.

6. The method according to claim 5, characterized in that The method further comprises: Control the servo motor of the line speed control part according to the PID compensation parameter to adjust the line speed during winding; The adjusted line speed value is obtained, and the adjusted line speed value and the type of winding craftwork are used as training sets. The connection weights of the RBF neural network are optimized according to the recursive least squares method to obtain an optimized RBF neural network, and the optimized RBF neural network is used to update the RBF neural network.

7. The method according to claim 2, characterized in that: The method further includes optimizing the preset fuzzy rule base and the Gaussian membership function by a genetic algorithm if the linear speed error exceeds a preset safety threshold within three consecutive sampling periods; Wherein, the optimizing the preset fuzzy rule base and the Gaussian membership function by genetic algorithm comprises: Encoding the preset fuzzy rule base and the Gaussian membership function to obtain an initialized population; The inverse of the sum of squares of the linear velocity errors in a plurality of consecutive sampling periods is used as a fitness function; Performing genetic operations on the fitness function and the initialized population based on a preset number of iterations to obtain a new population; The fitness value of the new population is calculated according to the fitness function to obtain the optimal individual, and the PID compensation model is updated by using the preset fuzzy rule base and the Gaussian membership function corresponding to the optimal individual to obtain an updated PID compensation model.

8. A winding tension control system, applied to a tension control device, the winding tension control device comprising a tension control part and a line speed control part, characterized in that: The system comprises: A tension data analysis module, used to obtain a real-time tension detection value and a tension setting value of the tension control part, and calculate a tension error and a tension error change rate based on the real-time tension detection value and the tension setting value; A linear speed data analysis module, used to obtain a real-time linear speed value and a linear speed setting value of the linear speed control part, and calculate a linear speed error and a linear speed error change rate based on the real-time linear speed value and the linear speed setting value; A tension control module, used for performing fuzzy reasoning on the tension error and the tension error change rate according to a fuzzy control algorithm, and obtaining a PID output parameter by using integral separation PID, wherein the PID output parameter is used for indicating a first servo motor that controls the tension control part; The linear speed control module is used to input the linear speed error and the linear speed error change rate into a PID compensation model based on the fuzzy control algorithm to obtain a PID compensation parameter, wherein the PID compensation parameter is used to indicate the second servo motor that controls the linear speed control part.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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